Ike Geiger Team Race Trophy

HostMIT
Date03/15/2014
TypeNOT ALLOWED In-Conference
BoatFJ
ScoringTeam

#	School	Team	Rec.
1	Tufts University	Jumbos	10–5
2	Bowdoin College	Polar Bears	10–5
3	Massachusetts Institute of Technology	Engineers	9–6
3	Roger Williams University	Hawks	9–6
5	Dartmouth College	Big Green	8–6
6	University of Rhode Island	Rams	5–9
7	Northeastern University	Huskies	0–14

Pre-event forecast

Fitted on the 2,456 team race matches sailed before 2014-03-15, using the line-ups that actually raced. It called 37 of 51 matches (72%) with a log-loss of 0.524 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness −0.71 ± 0.32: an upset-heavy event, more orderly than 6% of college team race events (temperature of the forecast log-odds that best fits the results, relative to the league average; nothing about the field, venue or wind predicts it, see the Weather tab). vs forecast is a team's wins minus the wins the model expected from the matches it sailed: positive means it beat its rating.

Place	School	Pred.	Strength	Boats (skipper / crew, boat value)	Pred. win %	Record	vs forecast
1	Tufts University Jumbos	4	9.70 school +0.09	Charles Proctor / Elizabeth Keys 2.99 Dan Nickerson / Jamie Maffeo 3.23 Duncan Swain / Julie Pringle 3.35	59%	10–5 (67%)	+1.1
2	Bowdoin College Polar Bears	5	9.51 school +0.38	Jack Mcguire / Julia Rew 3.24 6–4 Michael Croteau / Jackson Bloch 2.97 6–4 Peter Edmunds / Mary Paz 2.61 5–3 Tom Peabody / Mary Paz 2.86 3–1 Peter Edmunds / Julia Rew 2.90 2–1 Tom Peabody / Jackson Bloch 2.79 2–1 Jack Mcguire / Mary Paz 3.02 2–1 Tom Peabody / Julia Rew 3.03 2–0 Jack Mcguire / Jackson Bloch 3.06 2–0	55%	10–5 (67%)	+1.7
3	Massachusetts Institute of Technology Engineers	2	10.17 home +0.28	David Alfonso / Iris Xu 3.54 Joseph Paggi / Christopher Ford 3.02 Alexander Stewart / Paige Omura 3.21 7–5 Alexander Stewart / Jordan Ladd 3.21 2–1	71%	9–6 (60%)	−1.6
3	Roger Williams University Hawks	1	10.26 school +0.10	Stewart Draheim / Sara Swanson 3.08 Connor Needham / Samantha Bobo 3.58 J. Jacob / Tatiana Chiu 3.43	74%	9–6 (60%)	−2.1
5	Dartmouth College Big Green	3	9.35 school +0.07	Ian Storck / Madeleine Cooney Christopher Price / Sophie Kerr 2.83 4–5 Charles Lalumiere / Mia Steck 4–5 Charles Lalumiere / Sophie Kerr 4–1 Robert Floyd / Mia Steck 4–1	63%	8–6 (57%)	−0.8
6	University of Rhode Island Rams	6	6.32 school +0.30	Matthew Coughlin / Kaitlin Cornwall 2.73 Samuel Morley / Elizabeth Durgin Patrick Penwell / Matthew Sola 2.40	22%	5–9 (36%)	+1.9
7	Northeastern University Huskies	7	3.95	Zach Shapiro / Claire Lockard Victoria Mcgruer / Elizabeth Sczerzenie 1.53 John Hubbard / Emily Browning	2%	0–14 (0%)	−0.3

Surprisal: who beat their rating

Pre-event strength against the strength that would have made each record least surprising, everything else fixed. Δ is in the model's logit units, the same scale as the boat values above (a side is three boats plus the school term); green is a clear over-performance, red a clear under-performance. Skippers need three or more matches.

Place	School	Strength	Perf.	Record	Exp. wins	Δ	Surprise (bits)	P(this good or better)
6	University of Rhode Island	6.32	8.32	5–9	3.1	+2.00	3.3	3%
2	Bowdoin College	9.51	10.14	10–5	8.3	+0.64	0.8	23%
1	Tufts University	9.70	10.11	10–5	8.9	+0.42	0.3	35%
5	Dartmouth College	9.35	8.94	8–6	8.8	−0.41	0.2	82%
3	Massachusetts Institute of Technology	10.17	9.57	9–6	10.6	−0.60	0.7	90%
3	Roger Williams University	10.26	9.48	9–6	11.1	−0.78	1.2	95%
7	Northeastern University	3.95	0.95	0–14	0.3	≤−3.00	0.4	>99%

By skipper

Skipper	School	Value	Perf.	Record	Exp. wins	Δ	Surprise (bits)	P(this good or better)
Matthew Coughlin	University of Rhode Island	2.73	4.72	5–9	3.1	+2.00	3.3	3%
Samuel Morley	University of Rhode Island	0.56	2.55	5–9	3.1	+2.00	3.3	3%
Patrick Penwell	University of Rhode Island	2.40	4.39	5–9	3.1	+2.00	3.3	3%
Tom Peabody	Bowdoin College	2.86	3.82	7–2	5.9	+0.96	0.8	27%
Robert Floyd	Dartmouth College	2.67	3.63	4–1	3.5	+0.96	0.3	48%
Peter Edmunds	Bowdoin College	2.61	3.42	7–4	5.3	+0.80	1.0	19%
Jack Mcguire	Bowdoin College	3.24	3.87	10–5	8.3	+0.64	0.8	23%
Charles Proctor	Tufts University	2.99	3.40	10–5	8.9	+0.42	0.3	35%
Dan Nickerson	Tufts University	3.23	3.65	10–5	8.9	+0.42	0.3	35%
Duncan Swain	Tufts University	3.35	3.77	10–5	8.9	+0.42	0.3	35%
Michael Croteau	Bowdoin College	2.97	3.23	6–4	5.5	+0.26	0.1	49%
Ian Storck	Dartmouth College	2.74	2.33	8–6	8.8	−0.41	0.2	82%
Charles Lalumiere	Dartmouth College	3.15	2.73	8–6	8.8	−0.41	0.2	82%
David Alfonso	Massachusetts Institute of Technology	3.54	2.94	9–6	10.6	−0.60	0.7	90%
Alexander Stewart	Massachusetts Institute of Technology	3.21	2.61	9–6	10.6	−0.60	0.7	90%
Joseph Paggi	Massachusetts Institute of Technology	3.02	2.42	9–6	10.6	−0.60	0.7	90%
Connor Needham	Roger Williams University	3.58	2.79	9–6	11.1	−0.78	1.2	95%
J. Jacob	Roger Williams University	3.43	2.65	9–6	11.1	−0.78	1.2	95%
Christopher Price	Dartmouth College	2.83	1.96	4–5	5.2	−0.88	0.7	93%
Zach Shapiro	Northeastern University	1.15	-1.85	0–14	0.3	≤−3.00	0.4	>99%
Victoria Mcgruer	Northeastern University	1.53	-1.47	0–14	0.3	≤−3.00	0.4	>99%
John Hubbard	Northeastern University	0.68	-2.32	0–14	0.3	≤−3.00	0.4	>99%

Match by match

Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).

Rd	#	Winner	Loser	P(winner)
1	1	Massachusetts Institute of Technology	Northeastern University	100%
1	2	Roger Williams University	Dartmouth College	80%
1	3	Tufts University	Bowdoin College	61%
1	4	University of Rhode Island	Massachusetts Institute of Technology	2%	upset
1	5	Dartmouth College	Northeastern University	99%
1	6	Roger Williams University	Bowdoin College	75%
1	7	Tufts University	University of Rhode Island	97%
1	8	Dartmouth College	Massachusetts Institute of Technology	25%	upset
1	9	Bowdoin College	Northeastern University	100%
1	10	Tufts University	Roger Williams University	37%	upset
1	11	Dartmouth College	University of Rhode Island	96%
1	12	Massachusetts Institute of Technology	Bowdoin College	66%
1	13	Tufts University	Northeastern University	100%
1	14	Roger Williams University	University of Rhode Island	98%
1	15	Dartmouth College	Bowdoin College	44%	upset
1	16	Massachusetts Institute of Technology	Tufts University	61%
1	17	Roger Williams University	Northeastern University	100%
1	18	Bowdoin College	University of Rhode Island	97%
1	19	Tufts University	Dartmouth College	57%
1	20	Massachusetts Institute of Technology	Roger Williams University	47%	upset
1	21	University of Rhode Island	Northeastern University	91%
2	22	Massachusetts Institute of Technology	Northeastern University	100%
2	23	Dartmouth College	University of Rhode Island	96%
2	24	Bowdoin College	Roger Williams University	29%	upset
2	25	Tufts University	Massachusetts Institute of Technology	38%	upset
2	26	Dartmouth College	Northeastern University	100%
2	27	Bowdoin College	University of Rhode Island	96%
2	28	Roger Williams University	Tufts University	64%
2	29	Massachusetts Institute of Technology	Dartmouth College	69%
2	30	Bowdoin College	Northeastern University	100%
2	31	University of Rhode Island	Roger Williams University	2%	upset
2	32	Tufts University	Dartmouth College	53%
2	33	Bowdoin College	Massachusetts Institute of Technology	36%	upset
2	34	Roger Williams University	Northeastern University	100%
2	35	Tufts University	University of Rhode Island	96%
2	36	Bowdoin College	Dartmouth College	54%
2	37	Roger Williams University	Massachusetts Institute of Technology	53%
2	38	University of Rhode Island	Northeastern University	91%
2	39	Bowdoin College	Tufts University	49%	upset
2	40	Roger Williams University	Dartmouth College	66%
2	41	Massachusetts Institute of Technology	University of Rhode Island	98%
2	42	Tufts University	Northeastern University	100%
3	43	Massachusetts Institute of Technology	Tufts University	63%
3	44	Bowdoin College	Roger Williams University	30%	upset
3	46	Roger Williams University	Tufts University	64%
3	47	Bowdoin College	Massachusetts Institute of Technology	37%	upset
3	49	Massachusetts Institute of Technology	Roger Williams University	47%	upset
3	50	Tufts University	Bowdoin College	50%	upset
4	45	Dartmouth College	University of Rhode Island	96%
4	48	Dartmouth College	Northeastern University	100%
4	51	University of Rhode Island	Northeastern University	91%

Round 1

Team	Rec.	Jumbos	Bears	Engineers	Hawks	Green	Rams	Huskies
Tufts University Jumbos	5–1	✕	W	L	W	W	W	W
Bowdoin College Polar Bears	2–4	L	✕	L	L	L	W	W
Massachusetts Institute of Technology Engineers	4–2	W	W	✕	W	L	L	W
Roger Williams University Hawks	4–2	L	W	L	✕	W	W	W
Dartmouth College Big Green	4–2	L	W	W	L	✕	W	W
University of Rhode Island Rams	2–4	L	L	W	L	L	✕	W
Northeastern University Huskies	0–6	L	L	L	L	L	L	✕

Round 2

Team	Rec.	Jumbos	Bears	Engineers	Hawks	Green	Rams	Huskies
Tufts University Jumbos	4–2	✕	L	W	L	W	W	W
Bowdoin College Polar Bears	6–0	W	✕	W	W	W	W	W
Massachusetts Institute of Technology Engineers	3–3	L	L	✕	L	W	W	W
Roger Williams University Hawks	4–2	W	L	W	✕	W	L	W
Dartmouth College Big Green	2–4	L	L	L	L	✕	W	W
University of Rhode Island Rams	2–4	L	L	L	W	L	✕	W
Northeastern University Huskies	0–6	L	L	L	L	L	L	✕

Round 3

Team	Rec.	Jumbos	Bears	Engineers	Hawks
Tufts University Jumbos	1–2	✕	W	L	L
Bowdoin College Polar Bears	2–1	L	✕	W	W
Massachusetts Institute of Technology Engineers	2–1	W	L	✕	W
Roger Williams University Hawks	1–2	W	L	L	✕

Round 4

Team	Rec.	Green	Rams	Huskies
Dartmouth College Big Green	2–0	✕	W	W
University of Rhode Island Rams	1–1	L	✕	W
Northeastern University Huskies	0–2	L	L	✕

Filter by team:

#					Pos.
1	Engineers	1-2-3	def.	Huskies	4-5-6
2	Hawks	2-3-6	def.	Big Green	1-4-5 DSQ
3	Jumbos	1-2-6	def.	Polar Bears	3-4-5
4	Rams	1-3-5	def.	Engineers	2-4-6
5	Big Green	1-2-3	def.	Huskies	4-5-6
6	Hawks	1-2-3	def.	Polar Bears	4-5-6
7	Jumbos	1-3-4	def.	Rams	2-5-6
8	Big Green	1-2-6	def.	Engineers	3-4-5
9	Polar Bears	1-2-3	def.	Huskies	4-5-6
10	Jumbos	1-2-6	def.	Hawks	3-4-5
11	Big Green	2-3-4	def.	Rams	1-5-6
12	Engineers	2-3-4	def.	Polar Bears	1-5-6
13	Jumbos	1-2-3	def.	Huskies	4-5-6
14	Hawks	1-2-5	def.	Rams	3-4-6
15	Big Green	1-2-5	def.	Polar Bears	3-4-6
16	Engineers	2-3-4	def.	Jumbos	1-5-6
17	Hawks	1-2-5	def.	Huskies	3-4-6
18	Polar Bears	1-4-5	def.	Rams	2-3-6
19	Jumbos	2-3-4	def.	Big Green	1-5-6
20	Engineers	1-2-6	def.	Hawks	3-4-5
21	Rams	1-2-6	def.	Huskies	3-4-5
22	Engineers	1-2-3	def.	Huskies	4-5-6 DNS,DNS,DNS
23	Big Green	1-2-3	def.	Rams	4-5-6
24	Polar Bears	1-2-6	def.	Hawks	3-4-5
25	Jumbos	1-2-5	def.	Engineers	3-4-6
26	Big Green	1-2-3	def.	Huskies	4-5-6
27	Polar Bears	1-2-5	def.	Rams	3-4-6
28	Hawks	2-3-4	def.	Jumbos	1-5-6
29	Engineers	1-3-4	def.	Big Green	2-5-6
30	Polar Bears	1-2-3	def.	Huskies	4-5-6
31	Rams	2-3-4	def.	Hawks	1-5-6
32	Jumbos	2-3-5	def.	Big Green	1-4-6
33	Polar Bears	1-2-4	def.	Engineers	3-5-6
34	Hawks	1-2-4	def.	Huskies	3-5-6
35	Jumbos	1-2-3	def.	Rams	4-5-6
36	Polar Bears	1-3-6	def.	Big Green	2-4-5
37	Hawks	3-4-5	def.	Engineers	1-2-6 DSQ
38	Rams	1-2-4	def.	Huskies	3-5-6
39	Polar Bears	1-2-4	def.	Jumbos	3-5-6
40	Hawks	1-2-5	def.	Big Green	3-4-6
41	Engineers	1-3-4	def.	Rams	2-5-6
42	Jumbos	1-2-3	def.	Huskies	4-5-6
43	Engineers	1-2-4	def.	Jumbos	3-5-6
44	Polar Bears	2-5-6	def.	Hawks	1-3-4 DSQ
46	Hawks	1-2-5	def.	Jumbos	3-4-6
47	Polar Bears	1-2-4	def.	Engineers	3-5-6
49	Engineers	3-4-5	def.	Hawks	1-2-6 DSQ
50	Jumbos	1-2-4	def.	Polar Bears	3-5-6
45	Big Green	2-3-5	def.	Rams	1-4-6 DNS
48	Big Green	1-2-4	def.	Huskies	3-5-6
51	Rams	1-2-4	def.	Huskies	3-5-6

School	Team	Skippers	Crew
Tufts University	Jumbos	Charles Proctor '14 Dan Nickerson '15 Duncan Swain '15	Elizabeth Keys '14 Jamie Maffeo '15 Julie Pringle '14
Bowdoin College	Polar Bears	Jack Mcguire '17 Michael Croteau '15 Peter Edmunds '14 Tom Peabody '14	Julia Rew '16 Jackson Bloch '15 Mary Paz '17
Massachusetts Institute of Technology	Engineers	Alexander Stewart '17 David Alfonso '14 Joseph Paggi '16	Paige Omura '17 Jordan Ladd '17 Iris Xu '14 Christopher Ford '16
Roger Williams University	Hawks	Connor Needham '14 J. Jacob '16 Stewart Draheim '16	Samantha Bobo '15 Tatiana Chiu '17 Sara Swanson '16
Dartmouth College	Big Green	Charles Lalumiere '17 Christopher Price '14 Ian Storck '15 Robert Floyd '17	Sophie Kerr '17 Mia Steck '17 Madeleine Cooney '17
University of Rhode Island	Rams	Matthew Coughlin '16 Patrick Penwell '16 Samuel Morley '14	Kaitlin Cornwall '16 Matthew Sola '15 Elizabeth Durgin '16
Northeastern University	Huskies	John Hubbard '14 Victoria Mcgruer '17 Zach Shapiro '15	Emily Browning '16 Elizabeth Sczerzenie '15 Claire Lockard '18

Conditions — 03/15/2014 (8am–6pm)

☀️Clear sky

Humidity 64% · Pressure 1006.1 hPa

Temperature

47°F

↑ 53°F ↓ 38°F

Feels like 39°F

Wind

● from ▲ toward

11.0 kn

avg · max 12.1 kn

Gusts to 27.8 kn

From WSW (236°)

Sky & Precipitation

Cloud cover45% Sunshine9.0 hrs Humidity64% Pressure1006.1 hPa Rain0.05 in

What this breeze does at MIT — from fleet racing

Breeze: SW about 13 kn (weather reanalysis in the scorer’s units)

In fleet races at MIT in this breeze: an underdogs’ day (−0.03)

Underdogs’ conditions: in fleet racing here the fleet bunches up and lower-rated boats beat the seeds more often than usual.

Orderliness is how tightly a fleet race’s finishing order follows the pre-regatta ratings, relative to a typical event (0): positive is the favourites’ day, negative the underdogs’. A value of ±0.03 means a rating gap counts about 3% less than on a typical day. This event is not a fleet race the rating model scores, so there is no actual value for it: this is what this venue’s fleet racing history says about the breeze on the day, read off the chart at the star. How this is estimated · compare venues.

Orderliness beyond the fleet at MIT by wind direction (angle) and speed (rings), fitted surface; dots are labelled regatta-days, ★ this regatta (112 labelled fleet-race days at MIT).

From the notes

Short write-ups of what the ratings and results show, with the numbers and how they were computed.

How team race forecasts workThree boats’ ratings, a school term and a home edge; 78.8% of held-out matches called.
Play 1, Play 2, Play 4: how team races are wonHow often each play happens, how that shifts with breeze and level, and whether a star wins differently.
Which team races go to form?Orderliness is a real property of a team race regatta and nothing known in advance predicts it.

All notes →

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